沉浸式虚拟现实中动态难度调整过程中用户粘性的行为和心理生理测量

Oscar I. Caldas, M. Mauledoux, O. Avilés, C. R. Guerrero
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引用次数: 0

摘要

动态难度调整(DDA)已被广泛用于保持严肃和娱乐游戏的粘性,达到更好的学习效果,并提高用户表现。各种研究表明,在DDA中,任务表现(得分)会上升,直到达到与技能水平相关的平台期。然而,沉浸感是个体和情境相关的,DDA对沉浸式虚拟环境中其他沉浸指标的影响尚不清楚。本研究测量了研究对象在使用DDA玩沉浸式虚拟游戏时的参与度客观指标,以寻找动态反应的证据,类似于游戏表现。参与者被要求进行重复的上肢运动,同时记录以下指标:反应潜伏期作为知觉参与(感觉刺激后的时间),运动强度作为运动参与(手速度),心理生理反应作为情绪参与(心率,皮肤电导和呼吸频率)。此外,从信号中提取30个特征来评估它们在时间窗之间的变化。结果表明,在DDA过程中,反应潜伏期、垂直手速度和心率随着时间的推移发生了显著变化,并逐渐增长,直到达到一个平台,即受试者的最大表现。此外,从信号中提取的一些特征在时间窗之间存在显著差异,并且与得分的平均值有很强的相关性:最大潜伏期、y轴上的最小速度和平均心率,这表明在评估VR不同实验条件下的参与变化方面有很好的应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Behavioral and Psychophysiological Measures of Engagement During Dynamic Difficulty Adjustment in Immersive Virtual Reality
Dynamically Difficulty Adjustment (DDA) has been widely used to preserve engagement in serious and entertaining games, reach better learning, and enhance user performance. A variety of studies suggests that in DDA, task performance (score) rises until hitting a plateau associated with the skill level. However, the sense of engagement is individual and context-dependent, and the effect of DDA on other engagement indicators for immersive virtual environments is still unclear. This study measured objective indicators of engagement while study subjects played an immersive virtual game with DDA to find evidence of dynamic response, similar to game performance. Participants were demanded to perform repetitive upper-limb motions while recording the following indicators: Response Latency as perceptive engagement (elapsed time after sensory stimulus), Exercise Intensity as motion engagement (hand velocity), and psychophysiological responses as emotional engagement (Heart Rate, Skin Conductance, and Respiratory Rate). In addition, 30 features were extracted from the signals to evaluate their variations between time windows. Results indicate that response latency, vertical hand velocity, and heart rate showed significant changes over time during DDA and grew until hitting a plateau, i.e., at the subject's maximum performance. Moreover, some of the features extracted from the signals showed significant differences between time windows, and having strong correlation with the mean of score: max Latency, min velocity on the Y-axis, and mean heart rate, which suggest a promising application for evaluating changes in engagement between different experimental conditions in VR.
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